Table of contents

v.outlier removes outliers in a 3D point cloud. By default, the outlier
identification is done by a bicubic spline interpolation of the
observation with a high regularization parameter and a low resolution
in south-north and east-west directions. Those points that differ in
an absolute value more than the given threshold from a fixed value,
reckoned from its surroundings by the interpolation, are considered as
an outlier, and hence are removed.

The filter option specifies if all outliers will be removed
(default), or only positive or only negative outliers. Filtering out
only positive outliers can be useful to filter out vegetation returns
(e.g. from forest canopies) from LIDAR point clouds, in order to
extract Digital Terrain Models. Filtering out only negative outliers
can be useful to estimate vegetation height.

There is a flag to create a vector that can be visualizated by
qgis. That means that topology is build and the z coordinate is
considered as a category.